IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第2页

Dual-Population Evolution Based Dynamic Constrained Multiobjective Optimization With Discontinuous and Irregular Feasible Regions

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-18 DOI: 10.1109/TETCI.2025.3529882

Xiaoxu Jiang;Qingda Chen;Jinliang Ding;Xingyi Zhang

{"title":"Dual-Population Evolution Based Dynamic Constrained Multiobjective Optimization With Discontinuous and Irregular Feasible Regions","authors":"Xiaoxu Jiang;Qingda Chen;Jinliang Ding;Xingyi Zhang","doi":"10.1109/TETCI.2025.3529882","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529882","url":null,"abstract":"Dynamic constrained multiobjective optimization problems include irregular and discontinuous feasible regions, segmented true Pareto front, and dynamic environments. To address these problems, we design a dynamic constrained multiobjective optimization algorithm based on dual-population evolution. This algorithm includes two populations, P1 and P2, based on the feasibility of solutions. It utilizes valuable information from infeasible solutions to drive the populations toward the feasible regions and the true Pareto front. At the same time, we propose a mating selection operator to facilitate information exchange between populations and generate promising offspring solutions. To respond to environmental changes, we design a strategy that combines new solutions obtained by the sampling-selection-resampling method and updated old ones, rapidly generating a promising population in a new environment. Additionally, we also design a test suit that can effectively present the discontinuous feasible regions and the irregular changes of true Pareto front in practical appcation problems. The results from experiments demonstrate the efficacy of the test suit, and the proposed algorithm exhibits competitiveness compared to other algorithms.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1352-1366"},"PeriodicalIF":5.3,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Population Stream-Driven Scalable Evolutionary Many-Objective Optimization

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-17 DOI: 10.1109/TETCI.2025.3537916

Huangke Chen;Guohua Wu;Rui Wang;Witold Pedrycz

{"title":"Population Stream-Driven Scalable Evolutionary Many-Objective Optimization","authors":"Huangke Chen;Guohua Wu;Rui Wang;Witold Pedrycz","doi":"10.1109/TETCI.2025.3537916","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3537916","url":null,"abstract":"Solving multi-objective optimization problems with scalable decision variables and objectives is an ongoing challenging task. This study proposes a new evolutionary framework that a series of continuously generated subpopulations are used to approximate the entire Pareto-optimal front. These dynamic subpopulations are abstracted as a population stream. In this framework, one subpopulation is only responsible for searching for a Pareto-optimal solution. Diversity is emphasized among converged solutions coming from different subpopulations, striving to alleviate the conflict between diversity and convergence. To improve the convergence of the newly generated subpopulations, the polynomial fitting method is performed on the obtained solutions to model the relationships among decision variables, which are then used to assist in the generation of new subpopulations. Moreover, an adaptive granularity grid-based environmental selection strategy is proposed to maintain a set of well-diversifying converged solutions. Lastly, extensive experiments are conducted to demonstrate the proposal's superiority by comparing it with 19 representative algorithms in 45 test instances with 3-15 objectives and 300-1500 decision variables.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1406-1417"},"PeriodicalIF":5.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AdaFML: Adaptive Federated Meta Learning With Multi-Objectives and Context-Awareness in Dynamic Heterogeneous Networks

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-17 DOI: 10.1109/TETCI.2025.3537940

Qiaomei Han;Xianbin Wang;Weiming Shen;Yanjun Shi

{"title":"AdaFML: Adaptive Federated Meta Learning With Multi-Objectives and Context-Awareness in Dynamic Heterogeneous Networks","authors":"Qiaomei Han;Xianbin Wang;Weiming Shen;Yanjun Shi","doi":"10.1109/TETCI.2025.3537940","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3537940","url":null,"abstract":"Recent advancements in Federated Learning (FL) have enabled the widespread deployment of distributed computing resources across connected devices, enhancing data processing capabilities and facilitating collaborative decision-making while maintaining user privacy. However, in Internet of Things (IoT) systems, the heterogeneity of devices and unstable network connections present significant challenges to the effective and efficient execution of FL tasks in real-world environments. To address these challenges, we propose an Adaptive Federated Meta Learning Framework with Multi-Objectives and Context-Awareness (AdaFML). This framework aims to achieve multiple objectives, including improving the performance of the FL global model, optimizing time efficiency, and enabling local model adaptation in dynamic and heterogeneous environments. Specifically, AdaFML extracts contextual information from each device, including its data distribution, computation, and communication conditions, to train a multimodal model that optimizes the FL task and time cost estimation, enhancing global model performance and time efficiency. Moreover, AdaFML fine-tunes two critical meta-learning parameters: the mixture ratio between local and global models and the selection weights for model aggregation. This enables adaptive local model updates across different devices while improving global model performance. Experimental results demonstrate that AdaFML boosts the effectiveness, efficiency, and adaptability of FL task execution in dynamic and heterogeneous environments.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1428-1440"},"PeriodicalIF":5.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards the Explanation Consistency of Citizen Groups in Happiness Prediction via Factor Decorrelation

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-17 DOI: 10.1109/TETCI.2025.3537918

Xiaohua Wu;Lin Li;Xiaohui Tao;Jingling Yuan;Haoran Xie

{"title":"Towards the Explanation Consistency of Citizen Groups in Happiness Prediction via Factor Decorrelation","authors":"Xiaohua Wu;Lin Li;Xiaohui Tao;Jingling Yuan;Haoran Xie","doi":"10.1109/TETCI.2025.3537918","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3537918","url":null,"abstract":"The happiness level of citizen groups has been widely analyzed using machine learning methods with explanation, aiming to support informed decision-making in our society. However, caused of complex correlations between happiness factors, there is inconsistency in case-by-case explanations provided by different models. In response, we propose a novel and trustworthy explanation solution for happiness prediction that can identify a broadly acceptable key factor set to improve explanation consistency across various models. First, the factor decorrelation is employed to ensure competitively high prediction accuracy. Second, we utilized a happiness prediction model pool that includes trained models with competitive accuracy, contributing to consistent explanations. The factor contribution is then computed using a post-hoc method based on the Shapley value with theoretical properties. The final key factor set is determined by the intersection of sets across different models. Experimental results using the Chinese General Social Survey (CGSS) and the European Social Survey (ESS) datasets validate the 2-fold increase in explanation consistency. Represented by specific citizen groups built on <monospace>age</monospace>, comprised of young group (<inline-formula><tex-math>$leq$</tex-math></inline-formula>40) and elder group (<inline-formula><tex-math>$>$</tex-math></inline-formula>40), and <monospace>health</monospace>, comprised of bad health (1-3) and good health (4-5), we demonstrate how these demographics exhibit different contributions in terms of factors. Additionally, we leverage four objective metrics to further evaluate the explanation quality and a human perspective metric for evaluating explanation consistency by comparing our results against explanatory and descriptive studies to provide qualitative reliability measures.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1392-1405"},"PeriodicalIF":5.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trustworthy Federated Fine-Tuning for Industrial Chains Demand Forecasting

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-13 DOI: 10.1109/TETCI.2025.3537941

Guoquan Huang;Guanyu Lin;Li Ning;Yicheng Xu;Chee Peng Lim;Yong Zhang

{"title":"Trustworthy Federated Fine-Tuning for Industrial Chains Demand Forecasting","authors":"Guoquan Huang;Guanyu Lin;Li Ning;Yicheng Xu;Chee Peng Lim;Yong Zhang","doi":"10.1109/TETCI.2025.3537941","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3537941","url":null,"abstract":"Demand forecasting is crucial for the robust development of industrial chains, given the direct impact of consumer market volatility on production planning. However, in the intricate industrial chain environment, limited accessible data from independent production entities poses challenges in achieving high performances and precise predictions for future demand. Centralized training using machine learning modeling on data from multiple production entities is a potential solution, yet issues like consumer privacy, industry competition, and data security hinder practical machine learning implementation. This research introduces an innovative distributed learning approach, utilizing privacy-preserving federated learning techniques to enhance time-series demand forecasting for multiple entities pertaining to industrial chains. Our approach involves several key steps, including federated learning among entities in the industrial chain on a blockchain platform, ensuring the trustworthiness of the computation process and results. Leveraging Pre-training Models (PTMs) facilitates federated fine-tuning among production entities, addressing model heterogeneity and minimizing privacy breach risks. A comprehensive comparison study on various federated learning demand forecasting models on data from two real-world industry chains demonstrates the superior performance and enhanced security of our developed approach.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1441-1453"},"PeriodicalIF":5.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-13 DOI: 10.1109/TETCI.2025.3535656

Ping Guo;Cheng Gong;Xi Lin;Zhiyuan Yang;Qingfu Zhang

{"title":"Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume","authors":"Ping Guo;Cheng Gong;Xi Lin;Zhiyuan Yang;Qingfu Zhang","doi":"10.1109/TETCI.2025.3535656","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3535656","url":null,"abstract":"The escalating threat of adversarial attacks on deep learning models, particularly in security-critical fields, has highlighted the need for robust deep learning systems. Conventional evaluation methods of their robustness rely on adversarial accuracy, which measures the model performance under a specific perturbation intensity. However, this singular metric does not fully encapsulate the overall resilience of a model against varying degrees of perturbation. To address this issue, we propose a new metric termed as the adversarial hypervolume for assessing the robustness of deep learning models comprehensively over a range of perturbation intensities from a multi-objective optimization standpoint. This metric allows for an in-depth comparison of defense mechanisms and recognizes the trivial improvements in robustness brought by less potent defensive strategies. We adopt a novel training algorithm to enhance adversarial robustness uniformly across various perturbation intensities, instead of only optimizing adversarial accuracy. Our experiments validate the effectiveness of the adversarial hypervolume metric in robustness evaluation, demonstrating its ability to reveal subtle differences in robustness that adversarial accuracy overlooks.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1367-1378"},"PeriodicalIF":5.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Class Discriminative Knowledge Distillation

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-29 DOI: 10.1109/TETCI.2025.3529896

Shuoxi Zhang;Hanpeng Liu;Yuyi Wang;Kun He;Jun Lin;Yang Zeng

{"title":"Class Discriminative Knowledge Distillation","authors":"Shuoxi Zhang;Hanpeng Liu;Yuyi Wang;Kun He;Jun Lin;Yang Zeng","doi":"10.1109/TETCI.2025.3529896","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529896","url":null,"abstract":"Knowledge distillation aims to transfer knowledge from a large teacher model to a lightweight student model, enabling the student to achieve performance comparable to the teacher. Existing methods explore various strategies for distillation, including soft logits, intermediate features, and even class-aware logits. Class-aware distillation, in particular, treats the columns of logit matrices as class representations, capturing potential relationships among instances within a batch. However, we argue that representing class embeddings solely as column vectors may not fully capture their inherent properties. In this study, we revisit class-aware knowledge distillation and propose that effective transfer of class-level knowledge requires two regularization strategies: <italic>separability and <italic>orthogonality. Additionally, we introduce an asymmetric architecture design to further enhance the transfer of class-level knowledge. Together, these components form a new methodology, Class Discriminative Knowledge Distillation (CD-KD). Empirical results demonstrate that CD-KD significantly outperforms several state-of-the-art logit-based and feature-based methods across diverse visual classification tasks, highlighting its effectiveness and robustness.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1340-1351"},"PeriodicalIF":5.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IDET: Iterative Difference-Enhanced Transformers for High-Quality Change Detection

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-28 DOI: 10.1109/TETCI.2025.3529893

Qing Guo;Ruofei Wang;Rui Huang;Renjie Wan;Shuifa Sun;Yuxiang Zhang

{"title":"IDET: Iterative Difference-Enhanced Transformers for High-Quality Change Detection","authors":"Qing Guo;Ruofei Wang;Rui Huang;Renjie Wan;Shuifa Sun;Yuxiang Zhang","doi":"10.1109/TETCI.2025.3529893","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529893","url":null,"abstract":"Change detection (CD) is a crucial task in various real-world applications, aiming to identify regions of change between two images captured at different times. However, existing approaches mainly focus on designing advanced network architectures that map feature differences to change maps, overlooking the impact of feature difference quality. In this paper, we approach CD from a different perspective by exploring <italic>how to optimize feature differences to effectively highlight changes and suppress background regions. To achieve this, we propose a novel module called the iterative difference-enhanced transformers (IDET). IDET consists of three transformers: two for extracting long-range information from the bi-temporal images, and one for enhancing the feature difference. Unlike previous transformers, the third transformer utilizes the outputs of the first two transformers to guide iterative and dynamic enhancement of the feature difference. To further enhance refinement, we introduce the multi-scale IDET-based change detection approach, which utilizes multi-scale representations of the images to refine the feature difference at multiple scales. Additionally, we propose a coarse-to-fine fusion strategy to combine all refinements. Our final CD method surpasses nine state-of-the-art methods on six large-scale datasets across different application scenarios. This highlights the significance of feature difference enhancement and demonstrates the effectiveness of IDET. Furthermore, we demonstrate that our IDET can be seamlessly integrated into other existing CD methods, resulting in a substantial improvement in detection accuracy.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1093-1106"},"PeriodicalIF":5.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MENTOR: Guiding Hierarchical Reinforcement Learning With Human Feedback and Dynamic Distance Constraint

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-28 DOI: 10.1109/TETCI.2025.3529902

Xinglin Zhou;Yifu Yuan;Shaofu Yang;Jianye Hao

{"title":"MENTOR: Guiding Hierarchical Reinforcement Learning With Human Feedback and Dynamic Distance Constraint","authors":"Xinglin Zhou;Yifu Yuan;Shaofu Yang;Jianye Hao","doi":"10.1109/TETCI.2025.3529902","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529902","url":null,"abstract":"Hierarchical reinforcement learning (HRL) provides a promising solution for complex tasks with sparse rewards of agents, which uses a hierarchical framework that divides tasks into subgoals and completes them sequentially. However, current methods struggle to find suitable subgoals for ensuring a stable learning process. To address the issue, we propose a general hierarchical reinforcement learning framework incorporating human feedback and dynamic distance constraints, termed <bold>MENTOR, which acts as a “<italic>mentor”. Specifically, human feedback is incorporated into high-level policy learning to find better subgoals. Furthermore, we propose the Dynamic Distance Constraint (DDC) mechanism dynamically adjusting the space of optional subgoals, such that MENTOR can generate subgoals matching the low-level policy learning process from easy to hard. As a result, the learning efficiency can be improved. As for low-level policy, a dual policy is designed for exploration-exploitation decoupling to stabilize the training process. Extensive experiments demonstrate that MENTOR uses a small amount of human feedback to achieve significant improvement in complex tasks with sparse rewards.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1292-1306"},"PeriodicalIF":5.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Many-Objective Diversity-Guided Differential Evolution Algorithm for Multi-Label Feature Selection in High-Dimensional Datasets

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-27 DOI: 10.1109/TETCI.2025.3529840

Emrah Hancer;Bing Xue;Mengjie Zhang

{"title":"A Many-Objective Diversity-Guided Differential Evolution Algorithm for Multi-Label Feature Selection in High-Dimensional Datasets","authors":"Emrah Hancer;Bing Xue;Mengjie Zhang","doi":"10.1109/TETCI.2025.3529840","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529840","url":null,"abstract":"Multi-label classification (MLC) is crucial as it allows for a more nuanced and realistic representation of complex real-world scenarios, where instances may belong to multiple categories simultaneously, providing a comprehensive understanding of the data. Effective feature selection in MLC is paramount as it cannot only enhance model efficiency and interpretability but also mitigate the curse of dimensionality, ensuring more accurate and streamlined predictions for complex, multi-label data. Despite the proven efficacy of evolutionary computation (EC) techniques in enhancing feature selection for multi-label datasets, research on feature selection in MLC remains sparse in the domain of multi- and many-objective optimization. This paper proposes a many-objective differential evolution algorithm called MODivDE for feature selection in high-dimensional MLC tasks. The MODivDE algorithm involves multiple improvements and innovations in quality indicator-based selection, logic-based search strategy, and diversity-based archive update. The results demonstrate the exceptional performance of the MODivDE algorithm across a diverse range of high-dimensional datasets, surpassing recently introduced many-objective and conventional multi-label feature selection algorithms. The advancements in MODivDE collectively contribute to significantly improved accuracy, efficiency, and interpretability compared to state-of-the-art methods in the realm of multi-label feature selection.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1226-1237"},"PeriodicalIF":5.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0